{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Image Regression with CNNs.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/wilselby/diy_driverless_car_ROS/blob/ml-model/rover_ml/utils/Image_Regression_with_CNNs.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EPxyR5SargY9",
        "colab_type": "text"
      },
      "source": [
        "#Environment Setup\n",
        "\n",
        "##Set Flag for Local or Hosted Runtime"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LoJxV8hsrnhh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "local = 0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7vy5iiwR19nJ",
        "colab_type": "text"
      },
      "source": [
        "## Confirm TensorFlow can see the GPU \n",
        "\n",
        "Simply select \"GPU\" in the Accelerator drop-down in Notebook Settings (either through the Edit menu or the command palette at cmd/ctrl-shift-P)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_0h6Afcy2E9l",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f52ea28e-9fbd-4a20-caa5-2963f4a6e008"
      },
      "source": [
        "if not local:\n",
        "  import tensorflow as tf\n",
        "  device_name = tf.test.gpu_device_name()\n",
        "  if device_name != '/device:GPU:0':\n",
        "    raise SystemError('GPU device not found')\n",
        "  print('Found GPU at: {}'.format(device_name))"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found GPU at: /device:GPU:0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NI-UBX3-2WCU",
        "colab_type": "text"
      },
      "source": [
        "## Import Google Drive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LnbgHIgn2XsB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "a3d17a40-7c03-4896-83ad-a1bcd0f8639f"
      },
      "source": [
        "if not local:\n",
        "  # Load the Drive helper and mount\n",
        "  from google.colab import drive\n",
        "\n",
        "  # This will prompt for authorization.\n",
        "  drive.mount('/content/drive')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lZbZCuOD2JLH",
        "colab_type": "text"
      },
      "source": [
        "## Import Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AqUpKnu52L5S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "d27ffdd3-afc8-42e1-ca67-9624e8e6d3be"
      },
      "source": [
        "if not local:\n",
        "  import os\n",
        "  import csv\n",
        "  import cv2\n",
        "  import numpy as np\n",
        "  import matplotlib.pyplot as plt\n",
        "\n",
        "  import tensorflow as tf\n",
        "  tf.logging.set_verbosity(tf.logging.ERROR)\n",
        "\n",
        "  from keras import backend as K\n",
        "  from keras.models import Model, Sequential\n",
        "  from keras.layers import Dense, GlobalAveragePooling2D, MaxPooling2D, Lambda, Cropping2D\n",
        "  from keras.layers.convolutional import Convolution2D\n",
        "  from keras.layers.core import Flatten, Dense, Dropout, SpatialDropout2D\n",
        "  from keras.optimizers import Adam\n",
        "  from keras.callbacks import ModelCheckpoint, TensorBoard\n",
        "  from keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "  import sklearn\n",
        "  from sklearn.model_selection import train_test_split\n",
        "  import pandas as pd\n",
        "\n",
        "  # Improve progress bar display\n",
        "  import tqdm\n",
        "  import tqdm.auto\n",
        "  tqdm.tqdm = tqdm.auto.tqdm\n",
        "\n",
        "  print(tf.__version__)\n",
        "else:\n",
        "  import os\n",
        "  import csv\n",
        "  import cv2\n",
        "  import numpy as np\n",
        "  import matplotlib.pyplot as plt\n",
        "  import tensorflow as tf\n",
        "  from keras import backend as K\n",
        "  from keras.models import Model, Sequential\n",
        "  from keras.layers import Dense, GlobalAveragePooling2D, MaxPooling2D, Lambda, Cropping2D\n",
        "  from keras.layers.convolutional import Convolution2D\n",
        "  from keras.layers.core import Flatten, Dense, Dropout, SpatialDropout2D\n",
        "  from keras.optimizers import Adam\n",
        "  from keras.callbacks import ModelCheckpoint\n",
        "  import sklearn\n",
        "  from sklearn.model_selection import train_test_split\n",
        "  import pandas as pd"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1.14.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-rEBDAIHHWTD",
        "colab_type": "text"
      },
      "source": [
        "## Setup Tensorboard"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iKdshk3PRvMe",
        "colab_type": "code",
        "outputId": "8afc86fe-b27e-4b47-f4e7-7e1478046ebc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "if not local:\n",
        "  # Launch Tensorboard\n",
        "  # TODO https://www.tensorflow.org/tensorboard/r2/get_started\n",
        "\n",
        "  from tensorboardcolab import *\n",
        "\n",
        "  tbc = TensorBoardColab()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Wait for 8 seconds...\n",
            "TensorBoard link:\n",
            "http://359cc1b1.ngrok.io\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H2sPUxOR3FlN",
        "colab_type": "text"
      },
      "source": [
        "# Load Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8YsJN12w3aug",
        "colab_type": "text"
      },
      "source": [
        "## Extract Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ndnCqXG-3I7R",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d50b1940-66f9-4e75-9ef3-46c20abe901d"
      },
      "source": [
        "if not local:\n",
        "  path = '/content/drive/My Drive/research/diy_driverless_car_ROS/rover_ml/output'\n",
        "  data_set = 'office_delay_offset'\n",
        "  tar_file = data_set + \".tar.gz\"\n",
        "\n",
        "  os.chdir(path)\n",
        "\n",
        "  # Unzip the .tgz file\n",
        "  # x for extract\n",
        "  # -v for verbose \n",
        "  # -z for gnuzip\n",
        "  # -f for file (should come at last just before file name)\n",
        "  # -C to extract the zipped contents to a different directory\n",
        "  !if [ -d $data_set ]; then echo 'Directory Exists'; else tar -xvzf $tar_file ; fi\n",
        "else:\n",
        "  path = '/content/drive/My Drive/research/diy_driverless_car_ROS/rover_ml/output'\n",
        "  data_set = 'office_2'\n",
        "  tar_file = data_set + \".tar.gz\"\n"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Directory Exists\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9FogCRzr3sUF",
        "colab_type": "text"
      },
      "source": [
        "## Parse CSV File"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KeQ8c-9s3v8y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "fd91d75f-2505-4f16-be4d-5961437bb2df"
      },
      "source": [
        "# Define path to csv file\n",
        "if not local:\n",
        "  csv_path = data_set + '/interpolated.csv'\n",
        "else:\n",
        "  csv_path = path + '/' + data_set + '/interpolated.csv'\n",
        "\n",
        "# Load the CSV file into a pandas dataframe\n",
        "df = pd.read_csv(csv_path, sep=\",\")\n",
        "\n",
        "# Print the dimensions\n",
        "print(\"Dataset Dimensions:\")\n",
        "print(df.shape)\n",
        "\n",
        "# Print the first 5 lines of the dataframe for review\n",
        "print(\"\\nDataset Summary:\")\n",
        "df.head(5)\n",
        "  "
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dataset Dimensions:\n",
            "(3817, 8)\n",
            "\n",
            "Dataset Summary:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>index</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>width</th>\n",
              "      <th>height</th>\n",
              "      <th>frame_id</th>\n",
              "      <th>filename</th>\n",
              "      <th>angle</th>\n",
              "      <th>speed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-06-01 19:30:34.932834053</td>\n",
              "      <td>1559417434932834053</td>\n",
              "      <td>320</td>\n",
              "      <td>240</td>\n",
              "      <td>openmv_cam</td>\n",
              "      <td>center/1559417435032834053.jpg</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-06-01 19:30:35.043002986</td>\n",
              "      <td>1559417435043002986</td>\n",
              "      <td>320</td>\n",
              "      <td>240</td>\n",
              "      <td>openmv_cam</td>\n",
              "      <td>center/1559417435143002986.jpg</td>\n",
              "      <td>1.491693</td>\n",
              "      <td>0.098568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-06-01 19:30:35.149844074</td>\n",
              "      <td>1559417435149844074</td>\n",
              "      <td>320</td>\n",
              "      <td>240</td>\n",
              "      <td>openmv_cam</td>\n",
              "      <td>center/1559417435249844074.jpg</td>\n",
              "      <td>1.507192</td>\n",
              "      <td>0.098568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-06-01 19:30:35.252380037</td>\n",
              "      <td>1559417435252380037</td>\n",
              "      <td>320</td>\n",
              "      <td>240</td>\n",
              "      <td>openmv_cam</td>\n",
              "      <td>center/1559417435352380037.jpg</td>\n",
              "      <td>1.491543</td>\n",
              "      <td>0.099568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019-06-01 19:30:35.361442947</td>\n",
              "      <td>1559417435361442947</td>\n",
              "      <td>320</td>\n",
              "      <td>240</td>\n",
              "      <td>openmv_cam</td>\n",
              "      <td>center/1559417435461442947.jpg</td>\n",
              "      <td>0.904426</td>\n",
              "      <td>0.101248</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                           index            timestamp  ...     angle     speed\n",
              "0  2019-06-01 19:30:34.932834053  1559417434932834053  ...  0.000000  0.000000\n",
              "1  2019-06-01 19:30:35.043002986  1559417435043002986  ...  1.491693  0.098568\n",
              "2  2019-06-01 19:30:35.149844074  1559417435149844074  ...  1.507192  0.098568\n",
              "3  2019-06-01 19:30:35.252380037  1559417435252380037  ...  1.491543  0.099568\n",
              "4  2019-06-01 19:30:35.361442947  1559417435361442947  ...  0.904426  0.101248\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3BnBpo6-F2oR",
        "colab_type": "text"
      },
      "source": [
        "# Clean and Pre-process the Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ub8CkShSJEkV",
        "colab_type": "text"
      },
      "source": [
        "## Remove Unneccessary Columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PDth_K-3JINP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "2141db42-e217-41f9-c4d9-78f0e0db9576"
      },
      "source": [
        "# Remove 'index' and 'frame_id' columns \n",
        "df.drop(['index','frame_id'],axis=1,inplace=True)\n",
        "\n",
        "# Verify new dataframe dimensions\n",
        "print(\"Dataset Dimensions:\")\n",
        "print(df.shape)\n",
        "\n",
        "# Print the first 5 lines of the new dataframe for review\n",
        "print(\"\\nDataset Summary:\")\n",
        "print(df.head(5))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dataset Dimensions:\n",
            "(3817, 6)\n",
            "\n",
            "Dataset Summary:\n",
            "             timestamp  width  ...     angle     speed\n",
            "0  1559417434932834053    320  ...  0.000000  0.000000\n",
            "1  1559417435043002986    320  ...  1.491693  0.098568\n",
            "2  1559417435149844074    320  ...  1.507192  0.098568\n",
            "3  1559417435252380037    320  ...  1.491543  0.099568\n",
            "4  1559417435361442947    320  ...  0.904426  0.101248\n",
            "\n",
            "[5 rows x 6 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o-zof8fUDz2C",
        "colab_type": "text"
      },
      "source": [
        "## Detect Missing Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ffOXGmmQD2om",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "6be4a7d7-3420-4901-c4a6-a1b96f4928d2"
      },
      "source": [
        "# Detect Missing Values\n",
        "print(\"Any Missing Values?: {}\".format(df.isnull().values.any()))\n",
        "\n",
        "# Total Sum\n",
        "print(\"\\nTotal Number of Missing Values: {}\".format(df.isnull().sum().sum()))\n",
        "\n",
        "# Sum Per Column\n",
        "print(\"\\nTotal Number of Missing Values per Column:\")\n",
        "print(df.isnull().sum())"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Any Missing Values?: False\n",
            "\n",
            "Total Number of Missing Values: 0\n",
            "\n",
            "Total Number of Missing Values per Column:\n",
            "timestamp    0\n",
            "width        0\n",
            "height       0\n",
            "filename     0\n",
            "angle        0\n",
            "speed        0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cKBJ4sODIFOC",
        "colab_type": "text"
      },
      "source": [
        "## Remove Zero Throttle Values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QAk-fsbkIJrh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "d48098ac-2567-4e11-d1c8-d179de6637a9"
      },
      "source": [
        "# Determine if any throttle values are zeroes\n",
        "print(\"Any 0 throttle values?: {}\".format(df['speed'].eq(0).any()))\n",
        "\n",
        "# Determine number of 0 throttle values:\n",
        "print(\"\\nNumber of 0 throttle values: {}\".format(df['speed'].eq(0).sum()))\n",
        "\n",
        "# Remove rows with 0 throttle values\n",
        "if df['speed'].eq(0).any():\n",
        "  df = df.query('speed != 0')\n",
        "  \n",
        "  # Reset the index\n",
        "  df.reset_index(inplace=True)\n",
        "  \n",
        "# Verify new dataframe dimensions\n",
        "print(\"\\nNew Dataset Dimensions:\")\n",
        "print(df.shape)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Any 0 throttle values?: True\n",
            "\n",
            "Number of 0 throttle values: 1\n",
            "\n",
            "New Dataset Dimensions:\n",
            "(3816, 7)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6XZJLwqKCbE7",
        "colab_type": "text"
      },
      "source": [
        "## View Label Statistics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AgOG94fnCeDB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "outputId": "b7317ec7-d78f-49cb-8e7d-7b692652ce3c"
      },
      "source": [
        "# Steering Command Statistics\n",
        "print(\"\\nSteering Command Statistics:\")\n",
        "print(df['angle'].describe())\n",
        "\n",
        "print(\"\\nThrottle Command Statistics:\")\n",
        "# Throttle Command Statistics\n",
        "print(df['speed'].describe())"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Steering Command Statistics:\n",
            "count    3816.000000\n",
            "mean        0.826578\n",
            "std         0.932100\n",
            "min        -1.532910\n",
            "25%         0.111171\n",
            "50%         1.373665\n",
            "75%         1.481405\n",
            "max         1.541583\n",
            "Name: angle, dtype: float64\n",
            "\n",
            "Throttle Command Statistics:\n",
            "count    3816.000000\n",
            "mean        0.112546\n",
            "std         0.013608\n",
            "min         0.000379\n",
            "25%         0.105663\n",
            "50%         0.112759\n",
            "75%         0.120494\n",
            "max         0.150000\n",
            "Name: speed, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nae5TmmFFJ5T",
        "colab_type": "text"
      },
      "source": [
        "## View Histogram of Steering Commands"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JvPMKVGfFNdy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "c03eca9c-f7d5-46e4-88b7-a1922d039b28"
      },
      "source": [
        "num_bins = 25\n",
        "hist, bins = np.histogram(df['angle'], num_bins)\n",
        "print(bins)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[-1.53290974 -1.40993005 -1.28695035 -1.16397066 -1.04099097 -0.91801127\n",
            " -0.79503158 -0.67205188 -0.54907219 -0.4260925  -0.3031128  -0.18013311\n",
            " -0.05715342  0.06582628  0.18880597  0.31178566  0.43476536  0.55774505\n",
            "  0.68072474  0.80370444  0.92668413  1.04966382  1.17264352  1.29562321\n",
            "  1.4186029   1.5415826 ]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bh3DSasZQKCi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "fb4e2915-ddac-4619-e15c-6f9b537e2700"
      },
      "source": [
        "num_bins = 25\n",
        "samples_per_bin = 50\n",
        "hist, bins = np.histogram(df['angle'], num_bins)\n",
        "center = (bins[:-1]+ bins[1:]) * 0.5\n",
        "plt.bar(center, hist, width=0.05)\n",
        "plt.plot((np.min(df['angle']), np.max(df['angle'])), (samples_per_bin, samples_per_bin))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f890a1fd748>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD8CAYAAAB+UHOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cwunrGrDQweC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Normalize the histogram\n",
        "#print('total data:', len(df))\n",
        "#remove_list = []\n",
        "#for j in range(num_bins):\n",
        "#  list_ = []\n",
        "#  for i in range(len(df['angle'])):\n",
        "#    if df.loc[i,'angle'] >= bins[j] and df.loc[i,'angle'] <= bins[j+1]:\n",
        "#      list_.append(i)\n",
        "#  list_ = shuffle(list_)\n",
        "#  list_ = list_[samples_per_bin:]\n",
        "#  remove_list.extend(list_)\n",
        " \n",
        "#print('removed:', len(remove_list))\n",
        "#data.drop(data.index[remove_list], inplace=True)\n",
        "#print('remaining:', len(data))\n",
        " \n",
        "#hist, _ = np.histogram(data['steering'], (num_bins))\n",
        "#plt.bar(center, hist, width=0.05)\n",
        "#plt.plot((np.min(data['steering']), np.max(data['steering'])), (samples_per_bin, samples_per_bin))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1uqfxZ4uGoNX",
        "colab_type": "text"
      },
      "source": [
        "## View a Sample Image"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L2nwHnC4Gq1m",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348
        },
        "outputId": "65f9acad-2488-417d-9f85-4ae625ea70ee"
      },
      "source": [
        "# Crop values\n",
        "y = 100\n",
        "h = 100\n",
        "x = 0\n",
        "w = 320\n",
        "\n",
        "image[y:y+h, x:x+w]\n",
        "# View a Single Image \n",
        "num = 105\n",
        "img_name = path + '/' + data_set + '/' + df.loc[num,'filename']\n",
        "angle = df.loc[num,'angle']\n",
        "print(img_name)\n",
        "center_image = cv2.imread(img_name)\n",
        "crop_img = center_image[y:y+h, x:x+w]\n",
        "center_image_mod = cv2.resize(center_image, (320,180)) #resize from 720x1280 to 180x320\n",
        "\n",
        "plt.subplot(2,1,1)\n",
        "plt.imshow(center_image_mod)\n",
        "plt.grid(False)\n",
        "plt.xlabel('angle: {:.4}'.format(angle))\n",
        "plt.show() \n",
        "\n",
        "plt.subplot(2,1,2)\n",
        "plt.imshow(crop_img)\n",
        "plt.grid(False)\n",
        "plt.xlabel('angle: {:.4}'.format(angle))\n",
        "plt.show() "
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/research/diy_driverless_car_ROS/rover_ml/output/office_delay_offset/center/1559417446016623973.jpg\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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OKcINSGlBSLQDrEMgiEVMVuQkcQNTZORWoLVEqB1GTpIk5Hm+TY+bBgom9YGN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\ngoyIYAuCjIhgC4KMiGALgoyIYAuCjPgXqwwsyi7/9SQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1Dg4ar1RV1YB2kRVM5k94dvYzDg4O+I3f/IxvfPiNMBmo8hSFoCm6xkAISjOM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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gTQywtOyGvLv",
        "colab_type": "text"
      },
      "source": [
        "## View Multiple Images"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ODmdWWpsGxK2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "outputId": "75cb8cab-e9d6-4971-94f3-a342f2086c19"
      },
      "source": [
        "# Number of Images to Display\n",
        "num_images = 4\n",
        "\n",
        "# Display the images\n",
        "i = 0\n",
        "for i in range (0,num_images):\n",
        "    image_path = df.loc[i,'filename']\n",
        "    angle = df.loc[i,'angle']\n",
        "    img_name = path + '/' + data_set + '/' + image_path\n",
        "    image = cv2.imread(img_name)\n",
        "    image = cv2.resize(image, (320,180))\n",
        "    plt.subplot(num_images/2,num_images/2,i+1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.grid(False)\n",
        "    plt.imshow(image, cmap=plt.cm.binary)\n",
        "    plt.xlabel('angle: {:.3}'.format(angle))\n",
        "    i += 1"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IoWh3lsAYYq9Aui5WKowFBwmUkGX09/cZDIYE9RrthQ6OH7B3/Tppf4Cbl2ys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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jg1aMw8mGQ8o",
        "colab_type": "text"
      },
      "source": [
        "## Load the Images"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xkmkVIiKGStZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Define image loading function\n",
        "def load_images(dataframe):\n",
        "  \n",
        "  # initialize images array\n",
        "  images = []\n",
        "  \n",
        "  for i in df.index.values:\n",
        "    name = path + '/' + data_set + '/' + dataframe.loc[i,'filename'] \n",
        "    center_image = cv2.imread(name)\n",
        "    crop_img = center_image[100:200, 0:320]\n",
        "    center_image = cv2.resize(crop_img, (320,180))\n",
        "    images.append(center_image)\n",
        "    \n",
        "  return np.array(images)\n",
        "  \n",
        "# Load images   \n",
        "images = load_images(df) \n",
        "\n",
        "# Normalize image values\n",
        "images = images / 255.0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BgFvPAZl9vfP",
        "colab_type": "text"
      },
      "source": [
        "# Split the Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t2ibXOio_saZ",
        "colab_type": "text"
      },
      "source": [
        "## Create the feature set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Up2wjjIt-B1a",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "97271a9f-1d37-4183-cac6-4540321093a6"
      },
      "source": [
        "# partition the data into training and testing splits using 75% of\n",
        "# the data for training and the remaining 25% for testing\n",
        "split = train_test_split(df, images, test_size=0.25, random_state=42)\n",
        "(trainAttrX, testAttrX, trainImagesX, testImagesX) = split\n",
        "\n",
        "print(\"Number of training samples: {}\".format(trainAttrX.shape[0]))\n",
        "print(\"Number of validation samples: {}\".format(testAttrX.shape[0]))\n"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of training samples: 2862\n",
            "Number of validation samples: 954\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dSHgIhOy_Cxd",
        "colab_type": "text"
      },
      "source": [
        "## Create the label set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OMbWR4YW_Oe2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "trainY = trainAttrX[\"angle\"] \n",
        "testY = testAttrX[\"angle\"]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m3t9Inb5G7lp",
        "colab_type": "text"
      },
      "source": [
        "## Define a Batch Generator"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qh3kRBYuG9ed",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def csv_image_generator(dataframe, batch_size, mode=\"train\", aug=None):\n",
        "  \n",
        "  num_samples = dataframe.shape[0]\n",
        "  \n",
        "  # loop indefinitely\n",
        "  while True:\n",
        "    \n",
        "    for offset in range(0, num_samples, batch_size):\n",
        "      batch_samples = dataframe[offset:offset+batch_size]\n",
        "    \n",
        "      # initialize our batches of images and labels\n",
        "      #print(\"\\nLoaded batch {0}\\n\".format(1+(offset/batch_size)))\n",
        "      images = []\n",
        "      print(\"Init {}\".format(type(images)))\n",
        "      labels = []\n",
        "      index = 0\n",
        "      \n",
        "      for index in range(0,batch_samples.shape[0]):\n",
        "        if batch_samples.loc[offset,'filename'] != \"filename\":\n",
        "    \n",
        "          name = path + '/' + data_set + '/' + dataframe.loc[offset,'filename']\n",
        "          center_image = cv2.imread(name)\n",
        "          center_image = cv2.resize(center_image, (320,180))\n",
        "          label = dataframe.loc[offset,'angle']\n",
        "          print(\"Loop {}\".format(type(images)))\n",
        "          images.append(center_image)\n",
        "          labels.append(label)\n",
        "          \n",
        "          index += 1\n",
        "      \n",
        "          # if the data augmentation object is not None, apply it\n",
        "          if aug is not None:\n",
        "            (images, labels) = next(aug.flow(np.array(images), labels, batch_size=batch_size))\n",
        "          print(\"Aug {}\".format(type(images)))\n",
        " \n",
        "      # yield the batch to the calling function\n",
        "      yield (np.array(images), labels)\n",
        "        \n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PAVmOpT8HEg0",
        "colab_type": "text"
      },
      "source": [
        "## Define an Image Augmentation Data Generator"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gu67P9z2HIZV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Construct the training image generator for data augmentation\n",
        "aug = ImageDataGenerator(rotation_range=20, zoom_range=0.15,\n",
        "\twidth_shift_range=0.2, height_shift_range=0.2, shear_range=0.15,\n",
        "\tvertical_flip=True, fill_mode=\"nearest\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yA2di_mIdhvW",
        "colab_type": "text"
      },
      "source": [
        "## Initialize Data Generators"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dvhng7hsdiJQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Define a batch size\n",
        "batch_size = 32\n",
        "\n",
        "# initialize both the training and testing image generators\n",
        "trainGen = csv_image_generator(df, batch_size, mode=\"train\", aug=aug)\n",
        "testGen = csv_image_generator(df, batch_size, mode=\"train\", aug=None)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-_WW4C27_4HO",
        "colab_type": "text"
      },
      "source": [
        "# Train the Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xq73r642_6iI",
        "colab_type": "text"
      },
      "source": [
        "## Preprocess the Input Image"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "95obLCcw_8lT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Initialize the model\n",
        "model = Sequential()\n",
        "\n",
        "# trim image to only see section with road\n",
        "# (top_crop, bottom_crop), (left_crop, right_crop)\n",
        "model.add(Cropping2D(cropping=((50,20), (0,0)), input_shape=(180,320,3)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z62Wkaj4ADbB",
        "colab_type": "text"
      },
      "source": [
        "## Build the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AK578kaYAE1_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 785
        },
        "outputId": "5dd9b1cd-0d2e-4905-f697-21d20a44557b"
      },
      "source": [
        "# Nvidia model\n",
        "model.add(Convolution2D(24, (5, 5), activation=\"relu\", name=\"conv_1\", strides=(2, 2)))\n",
        "model.add(Convolution2D(36, (5, 5), activation=\"relu\", name=\"conv_2\", strides=(2, 2)))\n",
        "model.add(Convolution2D(48, (5, 5), activation=\"relu\", name=\"conv_3\", strides=(2, 2)))\n",
        "model.add(SpatialDropout2D(.5, dim_ordering='default'))\n",
        "\n",
        "model.add(Convolution2D(64, (3, 3), activation=\"relu\", name=\"conv_4\", strides=(1, 1)))\n",
        "model.add(Convolution2D(64, (3, 3), activation=\"relu\", name=\"conv_5\", strides=(1, 1)))\n",
        "\n",
        "model.add(Flatten())\n",
        "\n",
        "model.add(Dense(1164))\n",
        "model.add(Dropout(.5))\n",
        "model.add(Dense(100, activation='relu'))\n",
        "model.add(Dropout(.5))\n",
        "model.add(Dense(50, activation='relu'))\n",
        "model.add(Dropout(.5))\n",
        "model.add(Dense(10, activation='relu'))\n",
        "model.add(Dropout(.5))\n",
        "model.add(Dense(1))\n",
        "\n",
        "model.compile(loss='mse', optimizer='adam', metrics=['mse','mape'])\n",
        "\n",
        "# Print model sumamry\n",
        "model.summary()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: UserWarning: Update your `SpatialDropout2D` call to the Keras 2 API: `SpatialDropout2D(0.5, data_format=None)`\n",
            "  after removing the cwd from sys.path.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "cropping2d_1 (Cropping2D)    (None, 110, 320, 3)       0         \n",
            "_________________________________________________________________\n",
            "conv_1 (Conv2D)              (None, 53, 158, 24)       1824      \n",
            "_________________________________________________________________\n",
            "conv_2 (Conv2D)              (None, 25, 77, 36)        21636     \n",
            "_________________________________________________________________\n",
            "conv_3 (Conv2D)              (None, 11, 37, 48)        43248     \n",
            "_________________________________________________________________\n",
            "spatial_dropout2d_1 (Spatial (None, 11, 37, 48)        0         \n",
            "_________________________________________________________________\n",
            "conv_4 (Conv2D)              (None, 9, 35, 64)         27712     \n",
            "_________________________________________________________________\n",
            "conv_5 (Conv2D)              (None, 7, 33, 64)         36928     \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 14784)             0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1164)              17209740  \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 1164)              0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 100)               116500    \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 100)               0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 50)                5050      \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 50)                0         \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 10)                510       \n",
            "_________________________________________________________________\n",
            "dropout_4 (Dropout)          (None, 10)                0         \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 17,463,159\n",
            "Trainable params: 17,463,159\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "USKQYVmhAMaJ",
        "colab_type": "text"
      },
      "source": [
        "## Setup Checkpoints"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BS6R3FVoAOPc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c49e31c3-7ab8-4711-d896-c1de968e9ba5"
      },
      "source": [
        "# checkpoint\n",
        "model_path = data_set + '/model'\n",
        "\n",
        "!if [ -d $model_path ]; then echo 'Directory Exists'; else mkdir $model_path; fi\n",
        "\n",
        "\n",
        "filepath = path + '/' + model_path + \"/weights-improvement-{epoch:02d}-{val_loss:.2f}.hdf5\"\n",
        "checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='auto', period=1)\n",
        "\n",
        "#model.load_weights(model_path = '/')"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Directory Exists\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BVIGONgjSy7V",
        "colab_type": "text"
      },
      "source": [
        "## Setup Tensorboard"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-YWTAEeyS0tw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tbCallBack = TensorBoard(log_dir='./log', histogram_freq=1,\n",
        "                         write_graph=True,\n",
        "                         write_grads=True,\n",
        "                         batch_size=batch_size,\n",
        "                         write_images=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "71O-pWk9AQy3",
        "colab_type": "text"
      },
      "source": [
        "## Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3nkossmrAUo2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0598b3db-75fd-4e99-e3f4-2395c06ea9f2"
      },
      "source": [
        "# Define number of epochs\n",
        "n_epoch = 35\n",
        "\n",
        "# Define callbacks\n",
        "callbacks_list = [TensorBoardColabCallback(tbc)]\n",
        "\n",
        "# Fit the model\n",
        "#history_object = model.fit_generator(train_generator, steps_per_epoch=(len(train_samples) / batch_size_value), validation_data=validation_generator, validation_steps=(len(validation_samples)/batch_size_value), callbacks=callbacks_list, epochs=n_epoch)\n",
        "#history_object = model.fit_generator(\n",
        "#\ttrainGen,\n",
        "#\tsteps_per_epoch=trainAttrX.shape[0] // batch_size,\n",
        "#\tvalidation_data=testGen,\n",
        "#\tvalidation_steps=testAttrX.shape[0] // batch_size,\n",
        "#  callbacks=callbacks_list,\n",
        "#\tepochs=n_epoch)\n",
        "\n",
        "# Fit the model\n",
        "history_object = model.fit(trainImagesX, trainY, validation_data=(testImagesX, testY), callbacks=callbacks_list, epochs=n_epoch, batch_size=batch_size)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 2862 samples, validate on 954 samples\n",
            "Epoch 1/35\n",
            "2862/2862 [==============================] - 15s 5ms/step - loss: 1.3029 - mean_squared_error: 1.3029 - mean_absolute_percentage_error: 35843251.3986 - val_loss: 1.0658 - val_mean_squared_error: 1.0658 - val_mean_absolute_percentage_error: 39151797.8904\n",
            "Epoch 2/35\n",
            "2862/2862 [==============================] - 10s 3ms/step - loss: 1.0673 - mean_squared_error: 1.0673 - mean_absolute_percentage_error: 50487607.2418 - val_loss: 1.0173 - val_mean_squared_error: 1.0173 - val_mean_absolute_percentage_error: 45098459.9253\n",
            "Epoch 3/35\n",
            "2862/2862 [==============================] - 10s 3ms/step - loss: 1.0000 - mean_squared_error: 1.0000 - mean_absolute_percentage_error: 54559039.3322 - val_loss: 0.9282 - val_mean_squared_error: 0.9282 - val_mean_absolute_percentage_error: 58688594.1152\n",
            "Epoch 4/35\n",
            "2862/2862 [==============================] - 10s 3ms/step - loss: 0.9603 - mean_squared_error: 0.9603 - mean_absolute_percentage_error: 57515486.1728 - val_loss: 0.8944 - val_mean_squared_error: 0.8944 - val_mean_absolute_percentage_error: 66269225.3316\n",
            "Epoch 5/35\n",
            "2862/2862 [==============================] - 10s 3ms/step - loss: 0.9525 - mean_squared_error: 0.9525 - mean_absolute_percentage_error: 61621377.1357 - val_loss: 0.8966 - val_mean_squared_error: 0.8966 - val_mean_absolute_percentage_error: 65708897.5291\n",
            "Epoch 6/35\n",
            "2862/2862 [==============================] - 10s 3ms/step - loss: 0.9317 - mean_squared_error: 0.9317 - mean_absolute_percentage_error: 62642125.9220 - val_loss: 0.8825 - val_mean_squared_error: 0.8825 - val_mean_absolute_percentage_error: 69814456.1310\n",
            "Epoch 7/35\n",
            "2862/2862 [==============================] - 10s 3ms/step - loss: 0.9184 - mean_squared_error: 0.9184 - mean_absolute_percentage_error: 65493499.0329 - val_loss: 0.8869 - val_mean_squared_error: 0.8869 - val_mean_absolute_percentage_error: 68421043.3167\n",
            "Epoch 8/35\n",
            "2862/2862 [==============================] - 10s 3ms/step - loss: 0.9256 - mean_squared_error: 0.9256 - mean_absolute_percentage_error: 66003179.0380 - val_loss: 0.8741 - val_mean_squared_error: 0.8741 - val_mean_absolute_percentage_error: 72953700.6048\n",
            "Epoch 9/35\n",
            "2862/2862 [==============================] - 10s 3ms/step - loss: 0.9134 - mean_squared_error: 0.9134 - mean_absolute_percentage_error: 65745754.7549 - val_loss: 0.8699 - val_mean_squared_error: 0.8699 - val_mean_absolute_percentage_error: 74821955.2504\n",
            "Epoch 10/35\n",
            "2862/2862 [==============================] - 10s 3ms/step - loss: 0.8999 - mean_squared_error: 0.8999 - mean_absolute_percentage_error: 67301522.2179 - val_loss: 0.8702 - val_mean_squared_error: 0.8702 - val_mean_absolute_percentage_error: 74711433.3377\n",
            "Epoch 11/35\n",
            "2862/2862 [==============================] - 10s 3ms/step - loss: 0.9023 - mean_squared_error: 0.9023 - mean_absolute_percentage_error: 68876130.7473 - val_loss: 0.8687 - val_mean_squared_error: 0.8687 - val_mean_absolute_percentage_error: 75450465.4684\n",
            "Epoch 12/35\n",
            "2862/2862 [==============================] - 10s 3ms/step - loss: 0.9068 - mean_squared_error: 0.9068 - mean_absolute_percentage_error: 67861173.5185 - val_loss: 0.8671 - val_mean_squared_error: 0.8671 - val_mean_absolute_percentage_error: 76307940.3421\n",
            "Epoch 13/35\n",
            "2862/2862 [==============================] - 10s 3ms/step - loss: 0.8914 - mean_squared_error: 0.8914 - mean_absolute_percentage_error: 70225015.9054 - val_loss: 0.8672 - val_mean_squared_error: 0.8672 - val_mean_absolute_percentage_error: 76281244.6690\n",
            "Epoch 14/35\n",
            "2862/2862 [==============================] - 10s 3ms/step - loss: 0.8773 - mean_squared_error: 0.8773 - mean_absolute_percentage_error: 68033288.1208 - val_loss: 0.8683 - val_mean_squared_error: 0.8683 - val_mean_absolute_percentage_error: 75664835.0799\n",
            "Epoch 15/35\n",
            "2862/2862 [==============================] - 10s 3ms/step - loss: 0.8901 - mean_squared_error: 0.8901 - mean_absolute_percentage_error: 69129414.5105 - val_loss: 0.8693 - val_mean_squared_error: 0.8693 - val_mean_absolute_percentage_error: 75139556.0827\n",
            "Epoch 16/35\n",
            "2862/2862 [==============================] - 10s 3ms/step - loss: 0.8860 - mean_squared_error: 0.8860 - mean_absolute_percentage_error: 69398432.2941 - val_loss: 0.8672 - val_mean_squared_error: 0.8672 - val_mean_absolute_percentage_error: 76303219.0339\n",
            "Epoch 17/35\n",
            "2862/2862 [==============================] - 10s 3ms/step - loss: 0.8941 - mean_squared_error: 0.8941 - mean_absolute_percentage_error: 70672518.1086 - val_loss: 0.8771 - val_mean_squared_error: 0.8771 - val_mean_absolute_percentage_error: 71733021.4184\n",
            "Epoch 18/35\n",
            "2862/2862 [==============================] - 10s 3ms/step - loss: 0.8846 - mean_squared_error: 0.8846 - mean_absolute_percentage_error: 71272876.6419 - val_loss: 0.8630 - val_mean_squared_error: 0.8630 - val_mean_absolute_percentage_error: 79174162.3403\n",
            "Epoch 19/35\n",
            "2862/2862 [==============================] - 10s 3ms/step - loss: 0.8892 - mean_squared_error: 0.8892 - mean_absolute_percentage_error: 71562829.3148 - val_loss: 0.8663 - val_mean_squared_error: 0.8663 - val_mean_absolute_percentage_error: 76812565.5278\n",
            "Epoch 20/35\n",
            "2862/2862 [==============================] - 10s 3ms/step - loss: 0.8793 - mean_squared_error: 0.8793 - mean_absolute_percentage_error: 70326550.5150 - val_loss: 0.8629 - val_mean_squared_error: 0.8629 - val_mean_absolute_percentage_error: 79240122.6426\n",
            "Epoch 21/35\n",
            "2862/2862 [==============================] - 10s 3ms/step - loss: 0.8791 - mean_squared_error: 0.8791 - mean_absolute_percentage_error: 71805510.7628 - val_loss: 0.8639 - val_mean_squared_error: 0.8639 - val_mean_absolute_percentage_error: 78428187.4590\n",
            "Epoch 22/35\n",
            "2862/2862 [==============================] - 10s 3ms/step - loss: 0.8837 - mean_squared_error: 0.8837 - mean_absolute_percentage_error: 71328860.4429 - val_loss: 0.8621 - val_mean_squared_error: 0.8621 - val_mean_absolute_percentage_error: 79967561.8257\n",
            "Epoch 23/35\n",
            "2862/2862 [==============================] - 10s 3ms/step - loss: 0.8805 - mean_squared_error: 0.8805 - mean_absolute_percentage_error: 71439931.4637 - val_loss: 0.8610 - val_mean_squared_error: 0.8610 - val_mean_absolute_percentage_error: 81237155.6119\n",
            "Epoch 24/35\n",
            "2862/2862 [==============================] - 10s 3ms/step - loss: 0.8776 - mean_squared_error: 0.8776 - mean_absolute_percentage_error: 72168176.9956 - val_loss: 0.8611 - val_mean_squared_error: 0.8611 - val_mean_absolute_percentage_error: 81100809.5397\n",
            "Epoch 25/35\n",
            "2862/2862 [==============================] - 10s 3ms/step - loss: 0.8798 - mean_squared_error: 0.8798 - mean_absolute_percentage_error: 70953160.4357 - val_loss: 0.8620 - val_mean_squared_error: 0.8620 - val_mean_absolute_percentage_error: 80073903.3442\n",
            "Epoch 26/35\n",
            "2862/2862 [==============================] - 10s 3ms/step - loss: 0.8796 - mean_squared_error: 0.8796 - mean_absolute_percentage_error: 71609385.9715 - val_loss: 0.8614 - val_mean_squared_error: 0.8614 - val_mean_absolute_percentage_error: 80766694.5270\n",
            "Epoch 27/35\n",
            "2862/2862 [==============================] - 10s 3ms/step - loss: 0.8788 - mean_squared_error: 0.8788 - mean_absolute_percentage_error: 72181032.7609 - val_loss: 0.8622 - val_mean_squared_error: 0.8622 - val_mean_absolute_percentage_error: 79947377.4565\n",
            "Epoch 28/35\n",
            "2862/2862 [==============================] - 10s 3ms/step - loss: 0.8728 - mean_squared_error: 0.8728 - mean_absolute_percentage_error: 71667530.2916 - val_loss: 0.8614 - val_mean_squared_error: 0.8614 - val_mean_absolute_percentage_error: 80819220.9341\n",
            "Epoch 29/35\n",
            "2862/2862 [==============================] - 10s 3ms/step - loss: 0.8763 - mean_squared_error: 0.8763 - mean_absolute_percentage_error: 73946360.0855 - val_loss: 0.8610 - val_mean_squared_error: 0.8610 - val_mean_absolute_percentage_error: 81221937.8676\n",
            "Epoch 30/35\n",
            "2862/2862 [==============================] - 10s 3ms/step - loss: 0.8747 - mean_squared_error: 0.8747 - mean_absolute_percentage_error: 71886183.2949 - val_loss: 0.8608 - val_mean_squared_error: 0.8608 - val_mean_absolute_percentage_error: 81542218.1129\n",
            "Epoch 31/35\n",
            "2862/2862 [==============================] - 10s 3ms/step - loss: 0.8737 - mean_squared_error: 0.8737 - mean_absolute_percentage_error: 72956092.2492 - val_loss: 0.8609 - val_mean_squared_error: 0.8609 - val_mean_absolute_percentage_error: 81425751.5545\n",
            "Epoch 32/35\n",
            "2862/2862 [==============================] - 10s 3ms/step - loss: 0.8729 - mean_squared_error: 0.8729 - mean_absolute_percentage_error: 72404132.7315 - val_loss: 0.8613 - val_mean_squared_error: 0.8613 - val_mean_absolute_percentage_error: 80902243.5761\n",
            "Epoch 33/35\n",
            "2862/2862 [==============================] - 10s 3ms/step - loss: 0.8763 - mean_squared_error: 0.8763 - mean_absolute_percentage_error: 73061717.9325 - val_loss: 0.8622 - val_mean_squared_error: 0.8622 - val_mean_absolute_percentage_error: 79905059.6785\n",
            "Epoch 34/35\n",
            "2862/2862 [==============================] - 10s 3ms/step - loss: 0.8771 - mean_squared_error: 0.8771 - mean_absolute_percentage_error: 71503997.8074 - val_loss: 0.8624 - val_mean_squared_error: 0.8624 - val_mean_absolute_percentage_error: 79751968.1471\n",
            "Epoch 35/35\n",
            "2862/2862 [==============================] - 10s 3ms/step - loss: 0.8729 - mean_squared_error: 0.8729 - mean_absolute_percentage_error: 71750680.4110 - val_loss: 0.8608 - val_mean_squared_error: 0.8608 - val_mean_absolute_percentage_error: 81536517.4420\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OdaGfWNxBT4T",
        "colab_type": "text"
      },
      "source": [
        "## Save the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jWE--9r4BZEk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bcd16fa0-b50f-4521-ac8d-8bd3dae74c8d"
      },
      "source": [
        "# Save model\n",
        "model_path_full = path + '/' + model_path + '/'\n",
        "\n",
        "model.save(model_path_full + 'model.h5')\n",
        "with open(model_path_full + 'model.json', 'w') as output_json:\n",
        "    output_json.write(model.to_json())\n",
        "\n",
        "# Save TensorFlow model\n",
        "tf.train.write_graph(K.get_session().graph.as_graph_def(), logdir=model_path_full, name='model.pb', as_text=False)\n"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/drive/My Drive/research/diy_driverless_car_ROS/rover_ml/output/office_delay_offset/model/model.pb'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y-pCmYO1A89_",
        "colab_type": "text"
      },
      "source": [
        "# Evaluate the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "podNBS9cBRNW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#scores = model.evaluate(testImagesX, testY, verbose=0, batch_size=32)\n",
        "#print(\"Loss: {}\".format(scores*100))\n",
        "  \n",
        "#score = model.evaluate_generator(validation_generator, len(validation_samples)/batch_size_value,use_multiprocessing=True)\n",
        "#print(\"Loss: {:.2}\".format(score))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "caA5cztpBPTM",
        "colab_type": "text"
      },
      "source": [
        "## Compute the loss"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9FpUcNFu_5z6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "96450b57-af5c-4dac-8095-6568199b5d49"
      },
      "source": [
        "#TODO: Remove 0s in dataset\n",
        "\n",
        "preds = model.predict(testImagesX)\n",
        "\n",
        "\n",
        "diff = preds.flatten() - testY\n",
        "percentDiff = (diff / testY) * 100\n",
        "absPercentDiff = np.abs(percentDiff)\n",
        "\n",
        "#print(diff/testY)\n",
        "\n",
        "# compute the mean and standard deviation of the absolute percentage\n",
        "# difference\n",
        "mean = np.mean(absPercentDiff)\n",
        "std = np.std(absPercentDiff)\n",
        "print(\"[INFO] mean: {:.2f}%, std: {:.2f}%\".format(mean, std))"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] mean: inf%, std: nan%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5jVE6WfNGDEM",
        "colab_type": "text"
      },
      "source": [
        "## Plot a Prediction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O7o-6SbBD9zx",
        "colab_type": "text"
      },
      "source": [
        "## Plot the Results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t-M9fEvWD_ZJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        },
        "outputId": "0e1c52af-3ffd-4811-8e3c-0bb4d4b80766"
      },
      "source": [
        "model_path_full = path + '/' + model_path + '/'\n",
        "\n",
        "# Plot the training and validation loss for each epoch\n",
        "print('Generating loss chart...')\n",
        "plt.plot(history_object.history['loss'])\n",
        "plt.plot(history_object.history['val_loss'])\n",
        "plt.title('model mean squared error loss')\n",
        "plt.ylabel('mean squared error loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['training set', 'validation set'], loc='upper right')\n",
        "plt.savefig(model_path_full + 'model.png')\n",
        "\n",
        "# Done\n",
        "print('Done.')"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Generating loss chart...\n",
            "Done.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ICvAbYCGwHvhDQKMKpOzroLoE1rzS7OLI8DBOGZ7BB5vy8Xq7XRdPxhjTqjaT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\n9YLYAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QMGEoYbVHnaJ",
        "colab_type": "text"
      },
      "source": [
        "# References:\n",
        "https://www.pyimagesearch.com/2019/01/28/keras-regression-and-cnns/\n",
        "https://www.pyimagesearch.com/2019/01/21/regression-with-keras/\n",
        "https://www.pyimagesearch.com/2018/12/24/how-to-use-keras-fit-and-fit_generator-a-hands-on-tutorial/\n",
        "https://colab.research.google.com/github/tensorflow/examples/blob/master/courses/udacity_intro_to_tensorflow_for_deep_learning/l04c01_image_classification_with_cnns.ipynb#scrollTo=7MqDQO0KCaWS"
      ]
    }
  ]
}